scholarly journals Use the K-Neighborhood Subgraphs to Compute Canonical Labelings of Graphs

Mathematics ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. 690
Author(s):  
Jianqiang Hao ◽  
Yunzhan Gong ◽  
Jianzhi Sun ◽  
Li Tan

This paper puts forward an innovative theory and method to calculate the canonical labelings of graphs that are distinct to N a u t y ’s. It shows the correlation between the canonical labeling of a graph and the canonical labeling of its complement graph. It regularly examines the link between computing the canonical labeling of a graph and the canonical labeling of its o p e n k-n e i g h b o r h o o d s u b g r a p h. It defines d i f f u s i o n d e g r e e s e q u e n c e s and e n t i r e d i f f u s i o n d e g r e e s e q u e n c e. For each node of a graph G, it designs a characteristic m _ N e a r e s t N o d e to improve the precision for calculating canonical labeling. Two theorems established here display how to compute the first nodes of M a x Q ( G ) . Another theorem presents how to determine the second nodes of M a x Q ( G ) . When computing C m a x ( G ) , if M a x Q ( G ) already holds the first i nodes u 1 , u 2 , ⋯ , u i , Diffusion and Nearest Node theorems provide skill on how to pick the succeeding node of M a x Q ( G ) . Further, it also establishes two theorems to determine the C m a x ( G ) of disconnected graphs. Four algorithms implemented here demonstrate how to compute M a x Q ( G ) of a graph. From the results of the software experiment, the accuracy of our algorithms is preliminarily confirmed. Our method can be employed to mine the frequent subgraph. We also conjecture that if there is a node v ∈ S ( G ) meeting conditions C m a x ( G - v ) ⩽ C m a x ( G - w ) for each w ∈ S ( G ) ∧ w ≠ v , then u 1 = v for M a x Q ( G ) .

Author(s):  
Jürgen Jost ◽  
Raffaella Mulas ◽  
Florentin Münch

AbstractWe offer a new method for proving that the maxima eigenvalue of the normalized graph Laplacian of a graph with n vertices is at least $$\frac{n+1}{n-1}$$ n + 1 n - 1 provided the graph is not complete and that equality is attained if and only if the complement graph is a single edge or a complete bipartite graph with both parts of size $$\frac{n-1}{2}$$ n - 1 2 . With the same method, we also prove a new lower bound to the largest eigenvalue in terms of the minimum vertex degree, provided this is at most $$\frac{n-1}{2}$$ n - 1 2 .


Author(s):  
Shriya Sahu ◽  
Meenu Chawla ◽  
Nilay Khare ◽  
Bhasha Singh

2020 ◽  
Vol 595 ◽  
pp. 1-12
Author(s):  
Seyed Ahmad Mojallal ◽  
Pierre Hansen

Author(s):  
Jagannadha Rao D. B.

This paper addresses this issue and devises a new method for frequent subgraph mining in order to retrieve the valuable information from the database that captured the attention of the users. This paper proposes the recurrent-Gaston (R-Gaston) algorithm for the frequent subgraph mining process by enhancing the existing Gaston algorithm. Moreover, the method uses support measures based on the frequency and page duration parameters in order to define the support for the proposed R-Gaston algorithm. The simulation of the proposed R-Gaston is carried out using the weblog and the MSNBC databases. The proposed R-Gaston has attained values of number of structures mined and the execution time as 184, and 1282ms for the MSNBC database, with 60 and 75ms for the weblog database, respectively.


Author(s):  
Julian R. Eichhoff ◽  
Felix Baumann ◽  
Dieter Roller

In this paper we demonstrate and compare two complementary approaches to the automatic generation of production rules from a set of given graphs representing sample designs. The first approach generates a complete rule set from scratch by means of frequent subgraph discovery. Whereas the second approach is intended to learn additional rules that fit an existing, yet incomplete, rule set using genetic programming. Both approaches have been developed and tested in the context of an application for automated conceptual engineering design, more specifically functional decomposition. They can be considered feasible, complementary approaches to the automatic inference of graph rewriting rules for conceptual design applications.


Sign in / Sign up

Export Citation Format

Share Document